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Reworked the TensorExecutor code to support in place evaluation.

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This commit is contained in:
Benoit Steiner 2014-08-13 08:22:05 -07:00
parent 647622281e
commit 439feca139
1 changed files with 58 additions and 48 deletions
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106
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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@ -11,7 +11,7 @@
#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
#ifdef EIGEN_USE_THREADS
#include <future>
#include <future>"
#endif
namespace Eigen {
@ -28,45 +28,49 @@ namespace internal {
// Default strategy: the expression is evaluated with a single cpu thread.
template<typename Expression, typename Device = DefaultDevice, bool Vectorizable = TensorEvaluator<Expression, Device>::PacketAccess>
struct TensorExecutor
class TensorExecutor
{
public:
typedef typename Expression::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const Device& device = Device())
{
TensorEvaluator<Expression, Device> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded();
const Index size = evaluator.dimensions().TotalSize();
for (Index i = 0; i < size; ++i) {
evaluator.evalScalar(i);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize();
for (Index i = 0; i < size; ++i) {
evaluator.evalScalar(i);
}
}
evaluator.cleanup();
}
};
template<typename Expression>
struct TensorExecutor<Expression, DefaultDevice, true>
class TensorExecutor<Expression, DefaultDevice, true>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice())
{
TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded();
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize();
static const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
const int VectorizedSize = (size / PacketSize) * PacketSize;
const Index size = evaluator.dimensions().TotalSize();
static const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
const int VectorizedSize = (size / PacketSize) * PacketSize;
for (Index i = 0; i < VectorizedSize; i += PacketSize) {
evaluator.evalPacket(i);
for (Index i = 0; i < VectorizedSize; i += PacketSize) {
evaluator.evalPacket(i);
}
for (Index i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i);
}
}
for (Index i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i);
}
evaluator.cleanup();
}
};
@ -107,38 +111,40 @@ struct EvalRange<Evaluator, Index, true> {
};
template<typename Expression, bool Vectorizable>
struct TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const ThreadPoolDevice& device)
{
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
Evaluator evaluator(expr, device);
evaluator.evalSubExprsIfNeeded();
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = evaluator.dimensions().TotalSize();
const Index size = evaluator.dimensions().TotalSize();
static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
int blocksz = std::ceil<int>(static_cast<float>(size)/device.numThreads()) + PacketSize - 1;
const Index blocksize = std::max<Index>(PacketSize, (blocksz - (blocksz % PacketSize)));
const Index numblocks = size / blocksize;
int blocksz = std::ceil<int>(static_cast<float>(size)/device.numThreads()) + PacketSize - 1;
const Index blocksize = std::max<Index>(PacketSize, (blocksz - (blocksz % PacketSize)));
const Index numblocks = size / blocksize;
Index i = 0;
vector<std::future<void> > results;
results.reserve(numblocks);
for (int i = 0; i < numblocks; ++i) {
results.push_back(std::async(std::launch::async, &EvalRange<Evaluator, Index>::run, &evaluator, i*blocksize, (i+1)*blocksize));
}
Index i = 0;
vector<std::future<void> > results;
results.reserve(numblocks);
for (int i = 0; i < numblocks; ++i) {
results.push_back(std::async(std::launch::async, &EvalRange<Evaluator, Index>::run, &evaluator, i*blocksize, (i+1)*blocksize));
for (int i = 0; i < numblocks; ++i) {
results[i].get();
}
if (numblocks * blocksize < size) {
EvalRange<Evaluator, Index>::run(&evaluator, numblocks * blocksize, size);
}
}
for (int i = 0; i < numblocks; ++i) {
results[i].get();
}
if (numblocks * blocksize < size) {
EvalRange<Evaluator, Index>::run(&evaluator, numblocks * blocksize, size);
}
evaluator.cleanup();
}
};
@ -157,19 +163,23 @@ __global__ void EigenMetaKernel(Evaluator eval, unsigned int size) {
}
template<typename Expression, bool Vectorizable>
struct TensorExecutor<Expression, GpuDevice, Vectorizable>
class TensorExecutor<Expression, GpuDevice, Vectorizable>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const GpuDevice& device)
{
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
evaluator.evalSubExprsIfNeeded();
const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock();
const int block_size = maxCudaThreadsPerBlock();
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock();
const int block_size = maxCudaThreadsPerBlock();
const Index size = evaluator.dimensions().TotalSize();
EigenMetaKernel<TensorEvaluator<Expression, GpuDevice> > <<<num_blocks, block_size, 0, device.stream()>>>(evaluator, size);
eigen_assert(cudaGetLastError() == cudaSuccess);
const Index size = evaluator.dimensions().TotalSize();
EigenMetaKernel<TensorEvaluator<Expression, GpuDevice> > <<<num_blocks, block_size, 0, device.stream()>>>(evaluator, size);
assert(cudaGetLastError() == cudaSuccess);
}
evaluator.cleanup();
}
};